AI Research Papers
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The Definitive Guide to LLM Evaluation
A structured approach to building, implementing, and optimizing evaluation strategies for LLM applications.
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DeepLearning course: Evaluating AI Agents
Learn how to systematically assess and improve your AI agent’s performance in Evaluating AI Agents, a DeepLearning course.
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AI Benchmark Deep Dive: Gemini 2.5 and Humanity’s Last Exam
A comprehensive overview of modern AI benchmarks, taking a close look at Google’s recent Gemini 2.5 release and its performance on key evaluations
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Sleep-time Compute: Beyond Inference Scaling at Test-time
We recently discussed “Sleep Time Compute: Beyond Inference Scaling at Test Time,” new research from the team at Letta.
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Merge, Ensemble, and Cooperate! A Survey on Collaborative LLM Strategies
LLMs have revolutionized natural language processing, showcasing remarkable versatility and capabilities. But individual LLMs often exhibit distinct strengths and weaknesses, influenced by differences in their training corpora. This diversity poses a challenge: how can we maximize the efficiency and utility of large language models?
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Agent-as-a-Judge: Evaluate Agents with Agents
This week we dive into a paper that presents the “Agent-as-a-Judge” framework, a new paradigm for evaluating agent systems.
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Introduction to OpenAI’s Realtime API
We break down OpenAI’s realtime API. Sally-Ann DeLucia and Aparna Dhinakaran cover how to seamlessly integrate powerful language models into your applications for instant, context-aware responses that drive user engagement.
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Model Context Protocol (MCP) from Anthropic
Want to learn more about Anthropic’s groundbreaking Model Context Protocol (MCP)? We break down how this open standard is revolutionizing AI by enabling seamless integration between LLMs and external data sources, fundamentally transforming them into capable, context-aware agents.
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How DeepSeek is Pushing the Boundaries of AI Development
How do you train an AI model to think more like a human? That’s the challenge DeepSeek is tackling with its latest models, which push the boundaries of reasoning and reinforcement learning.
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Multiagent Finetuning: A Conversation with Researcher Yilun Du
This week we were excited to talk to Google DeepMind Senior Research Scientist (and incoming Assistant Professor at Harvard), Yilun Du, about his latest paper “Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains.”
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Swarm: OpenAI’s Experimental Approach to Multi-Agent Systems
As multi-agent systems grow in importance for fields ranging from customer support to autonomous decision-making, OpenAI has introduced Swarm, an experimental framework that simplifies the process of building and managing these systems.
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Breaking Down Reflection Tuning: Enhancing LLM Performance with Self-Learning
A recent announcement on X boasted a tuned model with pretty outstanding performance, and claimed these results were achieved through reflection tuning.
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